{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f912ff0-3fb1-4af2-83c2-f654332f79cd",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var_name</th>\n",
       "      <th>dim</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pwxqzgcs</td>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hjcfcs</td>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wghdbtcfcs</td>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tdlycfcs</td>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sdldjfbscs</td>\n",
       "      <td>S</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>zyjkcfcs</td>\n",
       "      <td>S</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lgcfcs</td>\n",
       "      <td>S</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>aqjgcs</td>\n",
       "      <td>S</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>xfaqcfcs</td>\n",
       "      <td>S</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cpzlcfcs</td>\n",
       "      <td>S</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mjjdbscs</td>\n",
       "      <td>S</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>czgqcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gqdjzrcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>swcfcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>jyyccs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dcdycs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>bdcdycs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>dwdbcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>qtajbscs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>wzjycfcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>xycfcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>qtxzcfcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>syhljbzdjzcfcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ldcfcs</td>\n",
       "      <td>G</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          var_name dim  num\n",
       "0         pwxqzgcs   E   50\n",
       "1           hjcfcs   E   50\n",
       "2       wghdbtcfcs   E   50\n",
       "3         tdlycfcs   E   50\n",
       "4       sdldjfbscs   S   50\n",
       "5         zyjkcfcs   S   50\n",
       "6           lgcfcs   S   50\n",
       "7           aqjgcs   S   50\n",
       "8         xfaqcfcs   S   50\n",
       "9         cpzlcfcs   S   50\n",
       "10        mjjdbscs   S   50\n",
       "11          czgqcs   G   50\n",
       "12        gqdjzrcs   G   50\n",
       "13          swcfcs   G   50\n",
       "14          jyyccs   G   50\n",
       "15          dcdycs   G   50\n",
       "16         bdcdycs   G   50\n",
       "17          dwdbcs   G   50\n",
       "18        qtajbscs   G   50\n",
       "19        wzjycfcs   G   50\n",
       "20          xycfcs   G   50\n",
       "21        qtxzcfcs   G   50\n",
       "22  syhljbzdjzcfcs   G   50\n",
       "23          ldcfcs   G   50"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import  pandas as pd\n",
    "\n",
    "pd.read_excel('config_rule2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfc49d02-022b-45a1-85bb-9cb4bb1e0e07",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import  pandas as pd\n",
    "pd.set_option('display.max_columns',None)\n",
    "pd.set_option('display.max_rows',600)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "lb= pd.read_excel('./好坏样本.xlsx')\n",
    "path = './21年数据分析结果/'\n",
    "df = pd.read_csv('./策略后esg分数2021.csv')\n",
    "\n",
    "map_dic = pd.read_csv('./model_map.csv',encoding='gbk').set_index('eng').to_dict()['china']\n",
    "# lb.to_csv('./bad_good.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45555da8-81e7-4aa7-a35d-b875e328a771",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7bb7c12f-61a3-44d3-b669-e65d25576076",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.describe()\n",
    "len(df) ,len(lb)\n",
    "lb.columns = ['custname','ify','l5']\n",
    "dic = {'正常':'A','关注':'B','次级':'C','可疑':'D','损失':'E'}\n",
    "lb['level'] = lb['l5'].map(dic)\n",
    "dic2 = {'N':0,'Y':1}\n",
    "lb['label'] = lb['ify'].map(dic2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0624b2b0-a93e-4cc1-8626-3381089c10f5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d = pd.merge(df,lb,on= 'custname')\n",
    "# d.model_type = d.model_type.map(map_dic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "42a3e9b2-cd50-49d2-b430-1e809423541d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "# plt.rcParams['font.family'] = ['Fang']\n",
    "\n",
    "# plt.figure(figsize = (3,4))\n",
    "# temp = r1.loc[r1.model_type == '交通运输、仓储和邮政业-不区分' ]\n",
    "\n",
    "# plt.bar(temp.ify,temp.esg_score)\n",
    "# plt.show()\n",
    "def plt_plot(r1,col1,col2):\n",
    "    fig,axs = plt.subplots(5,4,figsize=(12,7))\n",
    "    model_type_lst = r1.model_type.unique()\n",
    "    index = 0\n",
    "    for i in range(5):\n",
    "        for j in range(4):\n",
    "            if index > 18:\n",
    "                break\n",
    "\n",
    "            model_type = model_type_lst[index]\n",
    "            temp = r1.loc[r1.model_type == model_type]\n",
    "            axs[i,j].plot(temp[col1],temp[col2])\n",
    "            # axs[i,j].set_xlim(0,90)\n",
    "            axs[i,j].set_title(model_type)\n",
    "            index += 1\n",
    "    plt.tight_layout()        \n",
    "    plt.show()\n",
    "\n",
    "def plt_barh(r1,col1,col2):\n",
    "    fig,axs = plt.subplots(5,4,figsize=(12,7))\n",
    "    model_type_lst = r1.model_type.unique()\n",
    "    index = 0\n",
    "    for i in range(5):\n",
    "        for j in range(4):\n",
    "            if index > 18:\n",
    "                break\n",
    "\n",
    "            model_type = model_type_lst[index]\n",
    "            temp = r1.loc[r1.model_type == model_type]\n",
    "            axs[i,j].barh(temp[col1],temp[col2])\n",
    "            axs[i,j].set_xlim(0,90)\n",
    "            axs[i,j].set_title(model_type)\n",
    "            index += 1\n",
    "    plt.tight_layout()        \n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_hist(r1,col1):\n",
    "    \n",
    "    # result.esg_score.plot(kind='hist',bins=75)\n",
    "    # display(result.esg_score.describe())\n",
    "    fig,axs = plt.subplots(5,4,figsize=(12,7))\n",
    "    model_type_lst = r1.model_type.unique()\n",
    "    index = 0\n",
    "    for i in range(5):\n",
    "        for j in range(4):\n",
    "            if index > 18:\n",
    "                break\n",
    "\n",
    "            model_type = model_type_lst[index]\n",
    "            temp = r1.loc[r1.model_type == model_type]\n",
    "            axs[i,j].hist(temp[col1],bins=75,density=True,alpha=0.6)\n",
    "            axs[i,j].set_xlim(35,88)\n",
    "            # temp.esg_score.plot(kind='hist',bins=75)\n",
    "            axs[i,j].set_title(model_type)\n",
    "            index += 1\n",
    "    plt.tight_layout()        \n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "    \n",
    "# def plot_hist(r1,col1):\n",
    "    \n",
    "#     # result.esg_score.plot(kind='hist',bins=75)\n",
    "#     # display(result.esg_score.describe())\n",
    "#     plt.figure(figsize=(12,7))\n",
    "#     # fig,axs = plt.subplot(5,4)\n",
    "#     model_type_lst = r1.model_type.unique()\n",
    "#     index = 0\n",
    "#     for i in range(5):\n",
    "#         for j in range(4):\n",
    "#             if index > 18:\n",
    "#                 break\n",
    "#             plt.subplot(6,6,index+1)\n",
    "#             model_type = model_type_lst[index]\n",
    "#             temp = r1.loc[r1.model_type == model_type]\n",
    "#             sns.distplot(temp.esg_score,hist=True,bins=80)\n",
    "#             # temp.esg_score.plot(kind='hist',bins=75,density=True,alpha=0.6)\n",
    "#             # sns.displot(temp.esg_score)\n",
    "#             # plt.hist(temp.esg_score,density=True,bins=90)\n",
    "#             # axs[i,j].set_xlim(35,88)\n",
    "#             # temp.esg_score.plot(kind='hist',bins=75)\n",
    "#             plt.title(model_type)\n",
    "#             index += 1\n",
    "#     plt.tight_layout()        \n",
    "#     plt.show()\n",
    "\n",
    "\n",
    "\n",
    "def plot_hist2(result):\n",
    "    lst = result.model_type.unique()\n",
    "    for model_type in lst:\n",
    "        plt.figure(figsize = (5,3))\n",
    "        sns.distplot(result.loc[result.model_type==model_type].esg_score,hist=True,bins=80)\n",
    "        # display(result.esg_score.describe())\n",
    "        plt.title(model_type)\n",
    "        plt.xlim(30,88)\n",
    "        \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')  \n",
    "\n",
    "\n",
    "def plot_esg_rating():\n",
    "    fig,axs = plt.subplots(5,4,figsize=(12,7))\n",
    "    model_type_lst = r1.model_type.unique()\n",
    "    index = 0\n",
    "    for i in range(5):\n",
    "        for j in range(4):\n",
    "            if index > 18:\n",
    "                break\n",
    "\n",
    "            model_type = model_type_lst[index]\n",
    "            temp = r1.loc[r1.model_type == model_type]\n",
    "            \n",
    "            axs[i,j].bar(temp[col1],temp[col2])\n",
    "            axs[i,j].set_xlim(35,93)\n",
    "            # temp.esg_score.plot(kind='hist',bins=75)\n",
    "            axs[i,j].set_title(model_type)\n",
    "            index += 1\n",
    "    plt.tight_layout()        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e08061f1-dcc9-4a92-b53f-8406dadeab40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for model_type in d.model_type.unique():\n",
    "#     print(model_type)\n",
    "#     plt.figure(figsize=(4,3))\n",
    "#     tep = d.loc[d.model_type == model_type].esg_rating.value_counts()\n",
    "#     # tep.columns = ['esg_rating','num']\n",
    "#     tep.sort_index().plot(kind='bar')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97d886ec-7341-4f76-9aa8-995d78e18e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抚顺特殊钢股份有限公司\n",
    "# 金红叶纸业集团有限公司\n",
    "# 安吉租赁有限公司\n",
    "# 中国建筑第二工程局有限公司\n",
    "# 江苏南通二建集团有限公司\n",
    "# 中天建设集团有限公司\n",
    "# 河北建设集团股份有限公司\n",
    "# 庞大汽贸集团股份有限公司\n",
    "# 平安国际融资租赁有限公司\n",
    "# 上海易鑫融资租赁有限公司\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0041cbb6-2c34-48a1-ba70-51ccbc5effcd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ae6e5f3-ce08-48b0-8019-c3582895ff13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53393c84-b845-47f2-8fcf-14816e6d6151",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kLaof6lRQfv311wCAtLQ0vP322/Dz4z8IEZEUDE0mHDhXCQDwUrihzmDEN4XlDMoe0KVrlGvXrmVIEhFJqKC4CvWNRgzyVuC3o4IAAN8UlklcVf/U4RblzJkzsW7dOvj5+WHmzJl21922bVu3CyMiovZdvFoHAIi5wR8jg32xAyU4eP4qqhsa4avykLi6/qXDQalWqy0XidVqdY8VRERE16etbwQADPDywCAfJYYEeONcRS32nq7A78baHgiGuqbDQbl27VqbfyciIuczB6Xaq7n1OOnGQJyrqMX3Z64wKB2sS9co6+vrUVdXZ/n5woULyM7OxhdffOGwwoiIqH2WoPRsDsqbwpt7+k6WVktWU3/VpaCcPn061q9fDwCoqqpCQkIC3nrrLUyfPh05OTkOLZCIiKw1NBqhbzIBaA3KEcE+AIBTpTWS1dVfdSko8/PzMXHiRADA1q1bERISggsXLmD9+vV45513HFogERFZM7cmVR5yKN3dAADDg5qD8kqtgcPZOViXgrKurg6+vr4AgC+++AIzZ86EXC7HLbfcggsXLnR6fytXrkRUVBRUKhUSExNx4MCBdtfdtm0b4uPj4e/vD29vb8TGxuL999/vyscgIuqTzEHp79k66IuXwh03DPAEAJwqY6vSkboUlMOHD8f27dtRXFyMzz//HHfeeScAoKysrNPPV27evBnp6enIzMxEfn4+xo0bhylTpqCszPbzQAMHDsQLL7yAffv24ciRI0hLS0NaWho+//zzrnwUIqI+R1tnfX3S7Mbg5gbMKV6ndKguBWVGRgYWLVqEqKgoJCYmIikpCUBz63L8+PGd2teKFSswb948pKWlITo6Grm5ufDy8sKaNWtsrv+b3/wG9913H0aPHo1hw4Zh4cKFiImJwXfffWdzfb1eD51OZzUREfVl2gbbQWm5TskWpUN1KSgfeOABFBUV4eDBg9i1a5dl/uTJk/E///M/Hd6PwWDAoUOHkJyc3FqQXI7k5GTs27fvutsLIZCXl4fCwkL8+te/trlOVlYW1Gq1ZYqIiOhwfUREvZG5Ren3y6AMam5R8s5Xx+rSi5sBICQkBCEhIVbzEhISOrWPiooKGI1GBAcHW80PDg7GiRMn2t1Oq9UiPDwcer0ebm5u+Pvf/4477rjD5rpLly61vPUEAHQ6HcOSiPq01muUv+x6bW5RnmaL0qG6FJS1tbVYvnw58vLyUFZWBpPJZLX87NmzDimuPb6+vigoKEBNTQ3y8vKQnp6OoUOH4je/+U2bdZVKJZRKZY/WQ0TkTFW/GGzAzHzna0WNAbX6Jngru9wWomt06Sw+8cQT2LNnDx577DGEhoZ2+f1nAQEBcHNzQ2lpqdX80tLSNq3Va8nlcgwfPhwAEBsbi59//hlZWVk2g5KIqD8RQkBXb/sapfnO14tX61Fa3YChSh8pSux3uhSUO3fuxKeffopbb721WwdXKBSIi4tDXl4eZsyYAQAwmUzIy8vDM8880+H9mEwm6PV8boiI+j9dfRMMRuvBBq51Y7AvLl6tR5lOj6EBDEpH6FJQDhgwAAMHDnRIAenp6UhNTUV8fDwSEhKQnZ2N2tpapKWlAQDmzJmD8PBwZGVlAWi+OSc+Ph7Dhg2DXq/HZ599hvfff58jAhGRS7isrQfQ/A5KD7e292OOCPLBVyfKUFbNxoOjdCkoX3nlFWRkZOCf//wnvLy8ulVASkoKysvLkZGRAY1Gg9jYWOzatctyg09RURHk8tYvQ21tLebPn4+LFy/C09MTo0aNwoYNG5CSktKtOoiI+oKSlqC01ZoEgGGBLSP0cHQeh5EJIURnNxo/fjzOnDkDIQSioqLg4WH9D5afn++wAh1Np9NBrVZDq9Xy5dNE1Ods3H8BL3x0DKNCfDEnKcoyf1ZiJADgwLlKPPSPfRjg5YE/TRllta15HWrW0TzoUovSfD2RiIicq6SqAUD7LcohAd4AgKq6RjQaTTa7Z6lzuhSUmZmZjq6DiIg6oKKlS9VXZfvXd4CPAr5Kd1Trm1BZa0Cwn8qZ5fVLXf6/GlVVVXj33XexdOlSVFZWAmjucr106ZLDiiMiImtXag0A0O4zkjKZDEMCm1uVvE7pGF1qUR45cgTJyclQq9U4f/485s2bh4EDB2Lbtm0oKiqyvKuSiIgc62pLUHop2v/1HTXIG0cualFRY3BWWf1al1qU6enpmDt3Lk6dOgWVqrVZP3XqVHz77bcOK46IiKxV1plblG7trmO+TlnBFqVDdCkof/jhB/zhD39oMz88PBwajabbRRERkW2V5q5XOy3KoYHmoGSL0hG6FJRKpdLm66pOnjyJwMDAbhdFRERtNRlNlgHR7Y3jam5R8hqlY3QpKKdNm4aXX34ZjY3N/2AymQxFRUVYvHgx7r//focWSEREzarqG2F+8t3To/2u16iWoKzWN6Gh0eiM0vq1LgXlW2+9hZqaGgQGBqK+vh6TJk3C8OHD4evri1dffdXRNRIREVpv5PH0cIObvP2XUfipPCwtzivsfu22Lt31qlarsXv3buzduxeHDx9GTU0Nbr75ZqsXMBMRkWO1PhrSfmvSLMBHgVp9Eypq9Agf4NnTpfVrnQ5Kk8mEdevWYdu2bTh//nzzMztDhiAkJARCiC6/couIiOy72oEbecwCfJS4cKUOFbW8Ttldnep6FUJg2rRpeOKJJ3Dp0iXcdNNNGDNmDC5cuIC5c+fivvvu66k6iYhcnrlF6dWBFzIP8lYAACrZ9dptnWpRrlu3Dt9++y3y8vJw++23Wy376quvMGPGDKxfvx5z5sxxaJFERHRti/L6Xa8DvJqD8mpdY4/W5Ao61aL84IMP8Pzzz7cJSQD47W9/iyVLlmDjxo0OK46IiFpdb/i6a/l7NQ+aXlXHFmV3dSoojxw5gt/97nftLr/rrrtw+PDhbhdFRERtXa3rfItS19AIo6nTb1Oka3QqKCsrKy0vVLYlODgYV69e7XZRRETUVmUnrlH6qNzhJpfBJABdPbtfu6NTQWk0GuHu3v4/kJubG5qamrpdFBERtVXZiWuUcpkM/i3vrLxaz+7X7ujUzTxCCMydOxdKpdLmcr2etyETEfWUyk5cowSar1NeqTWgqrYRCOjJyvq3TgVlamrqddfhHa9ERI4nhGjteu3Ac5SA+TplLVuU3dSpoFy7dm1P1UFERHbUGYzQN5kAdGxkHuCaO19reY2yO7o01isRETmXuTWpcJdD4daxX92WZynZouwWBiURUR9gDspB3ooODxXq3xKUVRx0oFsYlEREfUBlyzOU5lZiRwxo6XrV1jXCJPgsZVcxKImI+gDzmK2DfDoelL4qD8hlgFEIVDfw0b2uYlASEfUBV7vQonSTy6D25FB23cWgJCLqA8zjvA707nhQAq3XKTk4etcxKImI+gBz12tng3IAB0fvtl4RlCtXrkRUVBRUKhUSExNx4MCBdtddvXo1Jk6ciAEDBmDAgAFITk62uz4RUX9gblF25holwBalI0gelJs3b0Z6ejoyMzORn5+PcePGYcqUKSgrK7O5/jfffINHHnkEX3/9Nfbt24eIiAjceeeduHTpkpMrJyJyniu1zUOEDvK2PYRoe9Sq5hZldQODsqskD8oVK1Zg3rx5SEtLQ3R0NHJzc+Hl5YU1a9bYXH/jxo2YP38+YmNjMWrUKLz77rswmUzIy8tzcuVERM5zpaXrNaCTLUpfz+YB2HQMyi6TNCgNBgMOHTqE5ORkyzy5XI7k5GTs27evQ/uoq6tDY2MjBg4caHO5Xq+HTqezmoiI+prKLt7M42duUdbz8ZCukjQoKyoqYDQa27zjMjg4GBqNpkP7WLx4McLCwqzC9lpZWVlQq9WWKSIiott1ExE5U0OjETX65qAb5NO5rldfVXOLskbfhCajyeG1uQLJu167Y/ny5fjwww/x0UcfQaVS2Vxn6dKl0Gq1lqm4uNjJVRIRdY/5Rh4PNxn8VJ16lwW8le6QywABoKKGd752RefOuIMFBATAzc0NpaWlVvNLS0sREhJid9s333wTy5cvx5dffomYmJh211Mqle2+P5OIqC+49tGQjo7zaiaXyeCr8oC2vhGlugaEqG03Kqh9krYoFQoF4uLirG7EMd+Yk5SU1O52f/nLX/DKK69g165diI+Pd0apRESSqejiHa9m5u7XUl2Dw2pyJZK2KAEgPT0dqampiI+PR0JCArKzs1FbW4u0tDQAzS+CDg8PR1ZWFgDg9ddfR0ZGBjZt2oSoqCjLtUwfHx/4+PhI9jmIiHrKlS6M83otX5UHgHqUVusdWJXrkDwoU1JSUF5ejoyMDGg0GsTGxmLXrl2WG3yKioogl7c2fHNycmAwGPDAAw9Y7SczMxMvvfSSM0snInKKSkuLsmtBab6uWc4WZZdIHpQA8Mwzz+CZZ56xueybb76x+vn8+fM9XxARUS/S2qLsatdr8yMipTq2KLuiT9/1SkTkCiq62fVqblGWVrNF2RUMSiKiXq7bXa+ebFF2B4OSiKiXswyI3s27Xst4jbJLGJRERL1cd+96NQ9jd6XWAEMTR+fpLAYlEVEvJoSwvDkkoIs383gp3ODWMlBBeQ27XzuLQUlE1IvVGYxoaGxuBXZ2QHQzmUxmeYsIBx3oPAYlEVEvZu52VXnI4aVw6/J+zN2vvE7ZeQxKIqJe7Nrh6zo7zuu1WoexY9drZzEoiYh6scpu3shj5mcZdIAtys5iUBIR9WJXuvkMpZkfW5RdxqAkIurFKro5fJ2Zb8ugA2UcnafTGJRERL2Y5RnKbrYo+aqtrmNQEhH1YuZgC/br3guX/TgwepcxKImIerFLVfUAgDB/z27txxyU2vpGNDQau12XK2FQEhH1YpdbgjK8m0Gp8pBD6d78K7+cL3DuFAYlEVEvpW8yoqwl1ML8u9f1KpPJLN23vE7ZOQxKIqJeqlTbHJJKd3mXh6+7VrBf852zvE7ZOQxKIqJe6tI13a7dGZXHLIgtyi5hUBIR9VKXHXQjj1mwb0tQ8lnKTmFQEhH1Uq1B2b3rk2bmrtcydr12CoOSiKiXuqxtDspQtYNalOx67RJ3qQsgIiLbLlU1B1pxZR027S/q9v6CLDfzMCg7gy1KIqJeytz16u/V/TtegdYWJbteO4dBSUTUCwkhWoOyZUDz7jIHZbW+CbX6Jofs0xUwKImIeiFtfSPqDM1Dzam9HBOUPkp3eCvcAMAykAFdH4OSiKgXMj9D6a1wg4eb435V84aezmNQEhH1QpdbbuRx1PVJs0Bf3tDTWZIH5cqVKxEVFQWVSoXExEQcOHCg3XV/+ukn3H///YiKioJMJkN2drbzCiUicqKSlkdD1A66PmnGG3o6T9Kg3Lx5M9LT05GZmYn8/HyMGzcOU6ZMQVlZmc316+rqMHToUCxfvhwhISFOrpaIyHkuXTXf8erooGwZdICj83SYpEG5YsUKzJs3D2lpaYiOjkZubi68vLywZs0am+v/6le/whtvvIGHH34YSqXSydUSETnPqbIaAHDIYOjXMrcoL2sZlB0lWVAaDAYcOnQIycnJrcXI5UhOTsa+ffscdhy9Xg+dTmc1ERH1dscvN/+uCnPQqDxmNwzwAgBcbGmx0vVJFpQVFRUwGo0IDg62mh8cHAyNRuOw42RlZUGtVlumiIgIh+2biKgnXKnRQ6NrgEwGhKodM86r2Q0DmoP30tU6h+63P+v3Q9gtXboU6enplp91Oh3Dkoh6tZ9aWpNRg7yh9HDr0DYdHeIuoqVFWVFjQL3BCE9Fx/bvyiQLyoCAALi5uaG0tNRqfmlpqUNv1FEqlbyeSUR9ijkoo8P8HL5vP093+CrdUa1vwqWqOgwP8nX4MfobybpeFQoF4uLikJeXZ5lnMpmQl5eHpKQkqcoiIpLcT5e1AIDoUMcHpUwmQ3hL92sxr1N2iKRdr+np6UhNTUV8fDwSEhKQnZ2N2tpapKWlAQDmzJmD8PBwZGVlAWi+Aej48eOWv1+6dAkFBQXw8fHB8OHDJfscRESOdLykuUU5JszPMvCAI0UM9MIJTTVv6OkgSYMyJSUF5eXlyMjIgEajQWxsLHbt2mW5waeoqAhyeWuj9/Llyxg/frzl5zfffBNvvvkmJk2ahG+++cbZ5RMROVytvgnnKmoBAGPC1D0SlOYbei5W8oaejpD8Zp5nnnkGzzzzjM1lvwy/qKgoCCGcUBURkTROaHQQAgjyVVqGm3M0PiLSOZIPYUdERK3MN/KM6YEbecwsLUo+ItIhDEoiol7kx6IqAD1zx6tZa1CyRdkRDEoiol7C0GTClz83PzL3m5FBPXYcc9frlVoD6gx8gfP1MCiJiHqJvWcqUN3QhCBfJeIiB/TYcdSeHvBTNd+icomtyutiUBIR9RI7j5YAAH43NgRyuaxHj2VuVRbzOuV1MSiJiHqBRqMJXxxv7na9a2xojx+P1yk7jkFJRNQL7D9biaq6RgzyViBhyMAePx4fEek4yZ+jJCJyVdcOZL7lYDEA4M4xIXDr4W5XABg8qDkoz5bX9Pix+jq2KImIJFZRo0dBcRUA4OFfOeftRqNbxpE1P7dJ7WNQEhFJ7OsTZRAAfjsqCOMi/J1yTPNzmiXaBlTU6J1yzL6KQUlEJKGK6tbW5LPJI5x2XB+lO4YGeANgq/J6GJRERBLa9ZMGAsCoEF/E3ODv1GOPCVcDaH2tF9nGoCQiksjxy1ocL9FBLgPujHbcC+s7yjye7E+X2KK0h0FJRCSB6oZGfHL4MgBg4ohAhKhVTq9hbFhzi/IYW5R2MSiJiCTw2mcnoGtowiBvBX47qufGdbXH3KK8cKUOuoZGSWroCxiUREROtuuYBh8cKIIMwIzx4fBwk+ZX8QBvBcL9m0foOc4betrFAQeIiJxIo23Akm1HAAATRwRgWKCPZdm1AxA4y5gwP1yqqsexS1rcMnSQ04/fF7BFSUTkJPomI57ZlI+qukbcFK5GcnSw1CVhbMudr+b3YFJbDEoiIicQQiDz459w8MJV+Krc8c4j4+Eul/5X8K9vDAQAfHWijO+mbIf0/0pERC7gmU0/4sMfiiEDcP/NN2DfmStSlwQAGHeDGoMHeaG+0YjdLW8vIWu8RklE1MO2HCzGpy3vmpwyJgQ3BvtKUoeta6CzEiNxb0wY/vb1afz78GVMjw2XoLLejS1KIqIe9HHBJSz+f80379w6bBAmjgiQuKK2psWGAQD2nCxHVZ1B4mp6HwYlEVEPEELgnbxTWPhhAUwCiB88AFNvCoVM1vOv0OqsG4N9MSrEF41GgV3HNFKX0+swKImIHKxEW4956w9ixe6TAIAnbhuCGePDe2VImplblTl7zqDeYJS4mt6FQUlE5CDl1Xpkf3kSyW/twZc/l8FdLsOr943Ff98TDXkvDkkAePSWwQhVq3DhSh3e/KJQ6nJ6Fd7MQ0TURUIInKuoxb6zV/D1iTJ8e7ICBqMJABA50Ku5FQmZJAMJdJafygOvzbwJaWt/wJq95zBlTAgShgyUuqxeQSaEEFIX4Uw6nQ5qtRparRZ+fn5Sl0NEfUhVnQEFxVUoKK7Cj0XNf2rrrcdIHR/pjxuDfXFTuLrXtyJtyS+6iq2HLsLTww1vPTQOU28KlbqkHtPRPOgVLcqVK1fijTfegEajwbhx4/DXv/4VCQkJ7a6/ZcsWvPjiizh//jxGjBiB119/HVOnTnVixUTUnzUZTTh/pRYnNNUo1FTj55JqFJbqUFxZ32Zdd7kM4f6euDGk+YaYULWnBBU7Tua90SjVNeB/T1Vg/sZ8TBsXht/fNgTjIvylLk0ykrcoN2/ejDlz5iA3NxeJiYnIzs7Gli1bUFhYiKCgtiPqf//99/j1r3+NrKws3HPPPdi0aRNef/115OfnY+zYsdc9HluUztdkNKGixoBSXQPKqvW4WmtAraEJhiYTPBVu8FW5I8TPE2H+KoSoVVC6u0ldMrkIo0mgRFuPs+W1KNRU44SmGic0Opwqq4GhyWRzm0HeCkQM9GqeBngiRK3qFSPsOMqsxEg0GU34y+eFWPXtWcv8iIGeSBwyCKND/TAkwAuDB3kjYoAXFO5997N3NA8kD8rExET86le/wt/+9jcAgMlkQkREBP74xz9iyZIlbdZPSUlBbW0tduzYYZl3yy23IDY2Frm5udc9nvnEbN1XCG8faR76lZoQgFEImETzNRaTEDCZAJMQEKL5T5Mw/9z6d6NJQN9kgqHJBIOx+c/Glj8NTSboW/7e0GiEtr4RVXWN0NY3QtfQiM58ywJ8lAjzVyFM7YlQfxWCfFXw9JBD4e4GpbscHu5yXNuh9cveLRmsZ1y7/JcdYeayhAAEhKVOgeZzg/aWC+t1RMv/iJY9ti43709YbSOE9fkGWn8W1/z92s9g/lzNf7c9v3V9meXzmufLrp1/zT4gk1ntz3wOrbdrnQ9b+7u2Hhv7aL8m6+M0n4e256v1nLbM/8W5tF7f+meI5u+trqEJuvpG6OobUV6jR3FlHS5V1aPRaPvLqXCTI9hPiWC/5v8DF9Lyp5eiV3TEOcWlq/X4/kwFjlzUwmjjP2K5DBjko8QgbwUG+SgwyFsJb6U7VB5yeHq4QeXhBpWHHCoPN7jL5ZDLALms+d9cLpNBLjf/LIObTAa5DPBWuluG1etpfaLr1WAw4NChQ1i6dKllnlwuR3JyMvbt22dzm3379iE9Pd1q3pQpU7B9+3ab6+v1euj1esvPWm3zC0oXrt8HudKrm5+AOqr5PwA3+Ko84K1wh4ebHG5yGRpNJjQYjM2/wBqa0GQSKNPXoewKUCB10eQS3GQy+Ht5IMhPiSA/JUJ8VQj0VWGAt8cvHucQQGM96lzotY0DFMDdo/2RPNwXxZX1KKqsxZVaAyprDaisbUSj0YTShjqUVjjumJEDPfHZwl87bod26HTNrxa7XntR0qCsqKiA0WhEcLD1CPrBwcE4ceKEzW00Go3N9TUa2w/JZmVlYdmyZW3mX8qZ27WiiYioxxQDUGc495jV1dVQq9XtLu/3fQhLly61aoFWVVVh8ODBKCoqsntiXIFOp0NERASKi4td/notz0UrnotWPBfN+ut5EEKguroaYWFhdteTNCgDAgLg5uaG0lLrEetLS0sREhJic5uQkJBOra9UKqFUKtvMV6vV/eofvDv8/Px4LlrwXLTiuWjFc9GsP56HjjSYJL1dSaFQIC4uDnl5eZZ5JpMJeXl5SEpKsrlNUlKS1foAsHv37nbXJyIi6g7Ju17T09ORmpqK+Ph4JCQkIDs7G7W1tUhLSwMAzJkzB+Hh4cjKygIALFy4EJMmTcJbb72Fu+++Gx9++CEOHjyIVatWSfkxiIion5I8KFNSUlBeXo6MjAxoNBrExsZi165dlht2ioqKIL/mGaUJEyZg06ZN+O///m88//zzGDFiBLZv396hZyiB5q7YzMxMm92xrobnohXPRSuei1Y8F81c/TxI/hwlERFRb9Z3h1QgIiJyAgYlERGRHQxKIiIiOxiUREREdvTboMzJyUFMTIzlAdmkpCTs3LnTsryhoQELFizAoEGD4OPjg/vvv7/NQAb90fLlyyGTyfDss89a5rnKuXjppZcgaxmA2TyNGjXKstxVzoPZpUuX8Oijj2LQoEHw9PTETTfdhIMHD1qWCyGQkZGB0NBQeHp6Ijk5GadOnZKw4p4RFRXV5nshk8mwYMECAK71vTAajXjxxRcxZMgQeHp6YtiwYXjllVd+8YIA1/heWBH91CeffCI+/fRTcfLkSVFYWCief/554eHhIY4dOyaEEOKpp54SERERIi8vTxw8eFDccsstYsKECRJX3bMOHDggoqKiRExMjFi4cKFlvquci8zMTDFmzBhRUlJimcrLyy3LXeU8CCFEZWWlGDx4sJg7d67Yv3+/OHv2rPj888/F6dOnLessX75cqNVqsX37dnH48GExbdo0MWTIEFFfXy9h5Y5XVlZm9Z3YvXu3ACC+/vprIYRrfS9effVVMWjQILFjxw5x7tw5sWXLFuHj4yPefvttyzqu8r24Vr8NSlsGDBgg3n33XVFVVSU8PDzEli1bLMt+/vlnAUDs27dPwgp7TnV1tRgxYoTYvXu3mDRpkiUoXelcZGZminHjxtlc5krnQQghFi9eLG677bZ2l5tMJhESEiLeeOMNy7yqqiqhVCrFBx984IwSJbNw4UIxbNgwYTKZXO57cffdd4vHH3/cat7MmTPF7NmzhRCu+73ot12v1zIajfjwww9RW1uLpKQkHDp0CI2NjUhOTrasM2rUKERGRrb7eq++bsGCBbj77rutPjMAlzsXp06dQlhYGIYOHYrZs2ejqKgIgOudh08++QTx8fF48MEHERQUhPHjx2P16tWW5efOnYNGo7E6H2q1GomJif3yfJgZDAZs2LABjz/+OGQymct9LyZMmIC8vDycPHkSAHD48GF89913uOuuuwC47vdC8pF5etLRo0eRlJSEhoYG+Pj44KOPPkJ0dDQKCgqgUCjg7+9vtb6913X1ZR9++CHy8/Pxww8/tFmm0Whc5lwkJiZi3bp1GDlyJEpKSrBs2TJMnDgRx44dc6nzAABnz55FTk4O0tPT8fzzz+OHH37Af/zHf0ChUCA1NdXymTvzSrv+YPv27aiqqsLcuXMBuNZ/HwCwZMkS6HQ6jBo1Cm5ubjAajXj11Vcxe/ZsAHDZ70W/DsqRI0eioKAAWq0WW7duRWpqKvbs2SN1WU5VXFyMhQsXYvfu3VCpVFKXIynz/ysGgJiYGCQmJmLw4MH417/+BU9PTwkrcz6TyYT4+Hi89tprAIDx48fj2LFjyM3NRWpqqsTVSee9997DXXfddd3XLvVX//rXv7Bx40Zs2rQJY8aMQUFBAZ599lmEhYW59PeiX3e9KhQKDB8+HHFxccjKysK4cePw9ttvIyQkBAaDAVVVVVbr23tdV1916NAhlJWV4eabb4a7uzvc3d2xZ88evPPOO3B3d0dwcLDLnItf8vf3x4033ojTp0+71HcCAEJDQxEdHW01b/To0ZauaPNn7swr7fq6Cxcu4Msvv8QTTzxhmedq34s//elPWLJkCR5++GHcdNNNeOyxx/Dcc89ZXkrhit8LoJ8H5S+ZTCbo9XrExcXBw8PD6nVdhYWFKCoq6nev65o8eTKOHj2KgoICyxQfH4/Zs2db/u4q5+KXampqcObMGYSGhrrUdwIAbr31VhQWFlrNO3nyJAYPHgwAGDJkCEJCQqzOh06nw/79+/vl+QCAtWvXIigoCHfffbdlnqt9L+rq6qxeQgEAbm5uMJlMAFzzewGg/z4esmTJErFnzx5x7tw5ceTIEbFkyRIhk8nEF198IYRovuU7MjJSfPXVV+LgwYMiKSlJJCUlSVy1c1x716sQrnMu/vM//1N888034ty5c2Lv3r0iOTlZBAQEiLKyMiGE65wHIZofFXJ3dxevvvqqOHXqlNi4caPw8vISGzZssKyzfPly4e/vLz7++GNx5MgRMX369H77GIDRaBSRkZFi8eLFbZa50vciNTVVhIeHWx4P2bZtmwgICBD/9V//ZVnHlb4XZv02KB9//HExePBgoVAoRGBgoJg8ebIlJIUQor6+XsyfP18MGDBAeHl5ifvuu0+UlJRIWLHz/DIoXeVcpKSkiNDQUKFQKER4eLhISUmxem7QVc6D2b///W8xduxYoVQqxahRo8SqVauslptMJvHiiy+K4OBgoVQqxeTJk0VhYaFE1faszz//XACw+flc6Xuh0+nEwoULRWRkpFCpVGLo0KHihRdeEHq93rKOK30vzPiaLSIiIjtc6holERFRZzEoiYiI7GBQEhER2cGgJCIisoNBSUREZAeDkoiIyA4GJRERkR0MSiIiIjsYlERERHYwKImIiOxgUBJRj2lsbJS6BKJuY1ASScBkMiErKwtDhgyBp6cnxo0bh61btwIArl69itmzZyMwMBCenp4YMWIE1q5da9n2+++/R2xsLFQqFeLj47F9+3bIZDIUFBRc97jX2/fFixfxyCOPYODAgfD29kZ8fDz2799vWZ6Tk4Nhw4ZBoVBg5MiReP/99632L5PJkJOTg2nTpsHb2xuvvvoqAODjjz/GzTffDJVKhaFDh2LZsmVoamrqzikkchp3qQsgckVZWVnYsGEDcnNzMWLECHz77bd49NFHERgYiC1btuD48ePYuXMnAgICcPr0adTX1wNofvffvffei6lTp2LTpk24cOECnn322Q4f98UXX2x33zU1NZg0aRLCw8PxySefICQkBPn5+ZZ3EX700UdYuHAhsrOzkZycjB07diAtLQ033HADbr/9dssxXnrpJSxfvhzZ2dlwd3fH//7v/2LOnDl45513MHHiRJw5cwZPPvkkACAzM9NBZ5SoB0n9+hIiV9PQ0CC8vLzE999/bzX/97//vXjkkUfEvffeK9LS0mxum5OTIwYNGmT17r/Vq1cLAOLHH3+87rHt7fsf//iH8PX1FVeuXLG5fMKECWLevHlW8x588EExdepUy88AxLPPPmu1zuTJk8Vrr71mNe/9998XoaGh162XqDdgi5LIyU6fPo26ujrccccdVvMNBgPGjx+Pl156Cffffz/y8/Nx5513YsaMGZgwYQIAoLCwEDExMVCpVJbtEhISOnzsp59+ut19FxQUYPz48Rg4cKDNbX/++WdLS9Ds1ltvxdtvv201Lz4+3urnw4cPY+/evZZuWAAwGo1oaGhAXV0dvLy8Olw/kRQYlEROVlNTAwD49NNPER4ebrVMqVQiIiICFy5cwGeffYbdu3dj8uTJWLBgAd58881uH/uuu+5qd9+enp7d3j8AeHt7W/1cU1ODZcuWYebMmW3WvTbwiXor3sxD5GTR0dFQKpUoKirC8OHDraaIiAgAQGBgIFJTU7FhwwZkZ2dj1apVAICRI0fi6NGj0Ov1lv398MMPnTp+e/uOiYlBQUEBKisrbW43evRo7N2712re3r17ER0dbfd4N998MwoLC9t81uHDh0Mu568g6v3YoiRyMl9fXyxatAjPPfccTCYTbrvtNmi1Wuzduxd+fn44c+YM4uLiMGbMGOj1euzYsQOjR48GAMyaNQsvvPACnnzySSxZsgRFRUWWlqZMJrvusTMyMtrd9yOPPILXXnsNM2bMQFZWFkJDQ/Hjjz8iLCwMSUlJ+NOf/oSHHnoI48ePR3JyMv79739j27Zt+PLLL697zHvuuQeRkZF44IEHIJfLcfjwYRw7dgx//vOfu3k2iZxA6oukRK7IZDKJ7OxsMXLkSOHh4SECAwPFlClTxJ49e8Qrr7wiRo8eLTw9PcXAgQPF9OnTxdmzZy3b7t27V8TExAiFQiHi4uLEpk2bBABx4sSJ6x73evs+f/68uP/++4Wfn5/w8vIS8fHxYv/+/Zblf//738XQoUOFh4eHuPHGG8X69eut9g9AfPTRR22Ou2vXLjFhwgTh6ekp/Pz8REJCgli1alUXzhyR88mEEELqsCairtu4cSPS0tKg1Woddp2RiFqx65Woj1m/fj2GDh2K8PBwHD58GIsXL8ZDDz3EkCTqIbySTtTHaDQaPProoxg9ejSee+45PPjgg5Ybcp566in4+PjYnJ566imJKyfqm9j1StSPlJWVQafT2Vzm5+eHoKAgJ1dE1PcxKImIiOxg1ysREZEdDEoiIiI7GJRERER2MCiJiIjsYFASERHZwaAkIiKyg0FJRERkx/8HcVBnmcEm7a8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_hist2(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39c0e73f-9746-496e-9bc6-2bff07b806e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x700 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(d,'esg_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe703ac9-3f3d-4e58-916a-80ec581d1747",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c173e92-b617-44c6-8673-8abce2158b65",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x700 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#pl好坏样本均分\n",
    "r1 = d.groupby(['model_type','ify']).esg_score.mean().reset_index()#.to_csv(path+'好坏样本均分.csv')\n",
    "plt_barh(r1,'ify','esg_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68ad6db5-7bd8-47e1-b483-822b002d28bc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#pl五级分类均分\n",
    "r2=d.groupby(['model_type','l5']).esg_score.mean().reset_index()#.to_csv(path + '五级分类均分.csv')\n",
    "r2['l5_class'] = r2.l5.map({'正常':'A','关注':'B','次级':'C','可疑':'D','损失':'E'})\n",
    "r2 = r2.sort_values('l5_class')\n",
    "# plt_plot(r2,'l5_class','esg_score')\n",
    "# r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e8aa206-e504-4079-88ac-0d133133dbad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x700 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#批量计算每个esg等级的坏样本数量\n",
    "left = d.groupby(['model_type','esg_rating']).label.sum().reset_index().rename(columns={'label':'label_count'})\n",
    "#按照模型统计坏样本数量后归一化展示esg等级对坏样本的区分度\n",
    "right = d.groupby(['model_type','esg_rating']).label.count().reset_index().rename(columns={'label':'label_all'})\n",
    "mege = pd.merge(left,right,on=['model_type','esg_rating'],how='left')\n",
    "mege['bad_ratio'] = round(mege.label_count / mege.label_all,4)\n",
    "r3 = mege.sort_values(['model_type','esg_rating'])#.to_csv(path + '各个模型好坏样本比率.csv')\n",
    "# right\n",
    "r3  =  r3.sort_values('esg_rating')\n",
    "plt_plot(r3,'esg_rating','bad_ratio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2855d593-0cac-4e1b-9eb4-3f6cc6667aa1",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>l5</th>\n",
       "      <th>model_type</th>\n",
       "      <th>esg_rating</th>\n",
       "      <th>关注</th>\n",
       "      <th>可疑</th>\n",
       "      <th>损失</th>\n",
       "      <th>次级</th>\n",
       "      <th>正常</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>B</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>C</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2</td>\n",
       "      <td>D</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A2</td>\n",
       "      <td>E</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B2</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B2</td>\n",
       "      <td>C</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B2</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B2</td>\n",
       "      <td>E</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C0</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C0</td>\n",
       "      <td>B</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>C0</td>\n",
       "      <td>C</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C0</td>\n",
       "      <td>D</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C0</td>\n",
       "      <td>E</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>C1</td>\n",
       "      <td>B</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>773.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>C1</td>\n",
       "      <td>C</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C1</td>\n",
       "      <td>D</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C1</td>\n",
       "      <td>E</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>D2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>D2</td>\n",
       "      <td>B</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>D2</td>\n",
       "      <td>C</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>D2</td>\n",
       "      <td>D</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>D2</td>\n",
       "      <td>E</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>E0</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>E0</td>\n",
       "      <td>B</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>E0</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>E0</td>\n",
       "      <td>D</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>E0</td>\n",
       "      <td>E</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>E1</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>E1</td>\n",
       "      <td>B</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>E1</td>\n",
       "      <td>C</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>917.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>E1</td>\n",
       "      <td>D</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>E1</td>\n",
       "      <td>E</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>F2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>F2</td>\n",
       "      <td>B</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>F2</td>\n",
       "      <td>C</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>F2</td>\n",
       "      <td>D</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>F2</td>\n",
       "      <td>E</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>G2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>G2</td>\n",
       "      <td>B</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>G2</td>\n",
       "      <td>C</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>G2</td>\n",
       "      <td>D</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>G2</td>\n",
       "      <td>E</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>H2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>H2</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>H2</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>H2</td>\n",
       "      <td>D</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>H2</td>\n",
       "      <td>E</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>I0</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>I0</td>\n",
       "      <td>B</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>I0</td>\n",
       "      <td>C</td>\n",
       "      <td>83.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>I0</td>\n",
       "      <td>D</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>I0</td>\n",
       "      <td>E</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>I1</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>I1</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>I1</td>\n",
       "      <td>C</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>I1</td>\n",
       "      <td>D</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>I1</td>\n",
       "      <td>E</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>J0</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>J0</td>\n",
       "      <td>B</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>J0</td>\n",
       "      <td>C</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>J0</td>\n",
       "      <td>D</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>J0</td>\n",
       "      <td>E</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>J1</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>J1</td>\n",
       "      <td>B</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>J1</td>\n",
       "      <td>C</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>J1</td>\n",
       "      <td>D</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>J1</td>\n",
       "      <td>E</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>K0</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>K0</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>K0</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>K0</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>K0</td>\n",
       "      <td>E</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>K1</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>K1</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>K1</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>378.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>K1</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>K1</td>\n",
       "      <td>E</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>L2</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>L2</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>L2</td>\n",
       "      <td>C</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>L2</td>\n",
       "      <td>D</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>L2</td>\n",
       "      <td>E</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>M0</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>M0</td>\n",
       "      <td>B</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>M0</td>\n",
       "      <td>C</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>M0</td>\n",
       "      <td>D</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>M0</td>\n",
       "      <td>E</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>M1</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>M1</td>\n",
       "      <td>B</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>M1</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>517.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>M1</td>\n",
       "      <td>D</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>M1</td>\n",
       "      <td>E</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "l5 model_type esg_rating    关注    可疑    损失    次级      正常\n",
       "0          A2          A   NaN   NaN   NaN   NaN     9.0\n",
       "1          A2          B   7.0   2.0   1.0   NaN   243.0\n",
       "2          A2          C  23.0   4.0   5.0   8.0   261.0\n",
       "3          A2          D   8.0   NaN   NaN   NaN    26.0\n",
       "4          A2          E   8.0   3.0   2.0   1.0    19.0\n",
       "5          B2          A   NaN   NaN   NaN   NaN     5.0\n",
       "6          B2          B   1.0   NaN   NaN   1.0   118.0\n",
       "7          B2          C  10.0   1.0   NaN   1.0   501.0\n",
       "8          B2          D   NaN   1.0   NaN   NaN    38.0\n",
       "9          B2          E   2.0   NaN   1.0   NaN    29.0\n",
       "10         C0          A   NaN   NaN   NaN   NaN    35.0\n",
       "11         C0          B   3.0   NaN   NaN   NaN   592.0\n",
       "12         C0          C  19.0   2.0   2.0  10.0  1283.0\n",
       "13         C0          D   8.0   NaN   2.0   4.0   189.0\n",
       "14         C0          E   2.0   1.0   2.0   2.0    48.0\n",
       "15         C1          B  19.0   1.0   2.0   3.0   773.0\n",
       "16         C1          C  42.0   6.0   3.0  13.0   408.0\n",
       "17         C1          D  13.0   1.0   3.0   2.0   103.0\n",
       "18         C1          E  15.0   5.0   3.0   5.0    45.0\n",
       "19         D2          A   NaN   NaN   NaN   NaN     7.0\n",
       "20         D2          B   2.0   NaN   NaN   1.0   339.0\n",
       "21         D2          C   6.0   1.0   3.0   3.0   366.0\n",
       "22         D2          D   3.0   3.0   NaN   NaN    93.0\n",
       "23         D2          E   2.0   1.0   1.0   NaN    33.0\n",
       "24         E0          A   1.0   NaN   NaN   NaN   259.0\n",
       "25         E0          B   5.0   NaN   1.0   2.0  1557.0\n",
       "26         E0          C   2.0   2.0   1.0   NaN   195.0\n",
       "27         E0          D  22.0   6.0   8.0  10.0  1583.0\n",
       "28         E0          E  12.0   1.0   6.0   3.0   172.0\n",
       "29         E1          A   1.0   NaN   NaN   NaN   135.0\n",
       "30         E1          B   4.0   1.0   NaN   NaN   179.0\n",
       "31         E1          C   6.0   NaN   1.0   2.0   917.0\n",
       "32         E1          D   9.0   3.0   NaN   NaN   352.0\n",
       "33         E1          E   9.0   1.0   2.0   3.0   102.0\n",
       "34         F2          A   NaN   NaN   NaN   1.0   191.0\n",
       "35         F2          B   3.0   1.0   1.0   3.0   557.0\n",
       "36         F2          C  15.0   7.0   3.0   5.0  1233.0\n",
       "37         F2          D   5.0   2.0   2.0   2.0   140.0\n",
       "38         F2          E   5.0   NaN   1.0   3.0    45.0\n",
       "39         G2          A   NaN   NaN   NaN   NaN    23.0\n",
       "40         G2          B   2.0   1.0   NaN   2.0   547.0\n",
       "41         G2          C  15.0   3.0   6.0  10.0  1422.0\n",
       "42         G2          D   6.0   2.0   1.0   5.0   105.0\n",
       "43         G2          E   4.0   1.0   4.0   3.0    23.0\n",
       "44         H2          A   NaN   NaN   NaN   NaN    21.0\n",
       "45         H2          B   1.0   NaN   NaN   NaN   237.0\n",
       "46         H2          C   NaN   NaN   NaN   NaN    10.0\n",
       "47         H2          D   3.0   NaN   2.0   NaN   119.0\n",
       "48         H2          E   3.0   NaN   NaN   2.0    27.0\n",
       "49         I0          A   1.0   NaN   NaN   NaN   105.0\n",
       "50         I0          B  10.0   3.0   1.0   6.0  2420.0\n",
       "51         I0          C  83.0  11.0  26.0  35.0  5122.0\n",
       "52         I0          D  13.0   3.0   5.0   6.0   248.0\n",
       "53         I0          E   7.0   2.0   9.0   3.0   189.0\n",
       "54         I1          A   NaN   NaN   NaN   NaN     6.0\n",
       "55         I1          B   1.0   NaN   NaN   NaN   205.0\n",
       "56         I1          C  32.0   3.0  11.0  10.0  1069.0\n",
       "57         I1          D   7.0   1.0   2.0   3.0    79.0\n",
       "58         I1          E  11.0   1.0   1.0   3.0    12.0\n",
       "59         J0          A   1.0   NaN   NaN   NaN   144.0\n",
       "60         J0          B   6.0   1.0   NaN   8.0   921.0\n",
       "61         J0          C  18.0   5.0   1.0   5.0  1366.0\n",
       "62         J0          D   7.0   2.0   2.0   2.0   411.0\n",
       "63         J0          E  14.0   2.0   4.0   3.0   137.0\n",
       "64         J1          A   NaN   NaN   NaN   NaN    26.0\n",
       "65         J1          B   4.0   NaN   NaN   1.0   353.0\n",
       "66         J1          C  12.0   2.0   1.0   3.0   477.0\n",
       "67         J1          D   4.0   NaN   1.0   2.0   119.0\n",
       "68         J1          E   5.0   2.0   2.0   4.0    29.0\n",
       "69         K0          A   NaN   NaN   NaN   NaN    53.0\n",
       "70         K0          B   NaN   NaN   NaN   NaN    56.0\n",
       "71         K0          C   2.0   NaN   NaN   NaN   634.0\n",
       "72         K0          D   2.0   NaN   NaN   NaN   185.0\n",
       "73         K0          E   4.0   2.0   1.0   2.0    38.0\n",
       "74         K1          A   NaN   NaN   NaN   NaN     7.0\n",
       "75         K1          B   NaN   NaN   NaN   NaN    74.0\n",
       "76         K1          C   2.0   NaN   NaN   NaN   378.0\n",
       "77         K1          D   2.0   NaN   NaN   NaN    83.0\n",
       "78         K1          E   3.0   NaN   NaN   NaN    21.0\n",
       "79         L2          A   NaN   NaN   NaN   NaN   155.0\n",
       "80         L2          B   1.0   1.0   NaN   1.0   377.0\n",
       "81         L2          C  10.0   3.0   1.0   1.0   674.0\n",
       "82         L2          D   1.0   NaN   2.0   2.0   128.0\n",
       "83         L2          E   6.0   NaN   4.0   NaN    20.0\n",
       "84         M0          A   NaN   NaN   NaN   NaN    47.0\n",
       "85         M0          B  15.0   NaN   4.0   4.0  1016.0\n",
       "86         M0          C  35.0   9.0   8.0   6.0  1717.0\n",
       "87         M0          D  14.0   3.0   4.0   9.0   277.0\n",
       "88         M0          E  14.0   5.0   8.0   7.0   146.0\n",
       "89         M1          A   1.0   NaN   NaN   NaN    27.0\n",
       "90         M1          B  10.0   NaN   NaN   NaN   411.0\n",
       "91         M1          C  26.0   4.0   7.0   4.0   517.0\n",
       "92         M1          D  20.0   3.0   1.0   7.0    72.0\n",
       "93         M1          E   8.0   NaN   5.0   2.0    23.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#批量计算 esg评级对五级分类的联合分布计数\n",
    "r4 = d.groupby(['model_type','esg_rating','l5']).custname.count().unstack().reset_index()#.to_csv(path + '五级分类与esg等级的联合分布.csv')\n",
    "r4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80d06faf-1ce2-44c8-931d-46377c2c5644",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_type</th>\n",
       "      <th>auc</th>\n",
       "      <th>ks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>J0</td>\n",
       "      <td>0.637544</td>\n",
       "      <td>0.310224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>I0</td>\n",
       "      <td>0.657825</td>\n",
       "      <td>0.227697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>D2</td>\n",
       "      <td>0.666008</td>\n",
       "      <td>0.297143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G2</td>\n",
       "      <td>0.668385</td>\n",
       "      <td>0.275986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F2</td>\n",
       "      <td>0.675976</td>\n",
       "      <td>0.247202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>0.700770</td>\n",
       "      <td>0.349880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.710882</td>\n",
       "      <td>0.439071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C0</td>\n",
       "      <td>0.721642</td>\n",
       "      <td>0.293451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M0</td>\n",
       "      <td>0.737148</td>\n",
       "      <td>0.383970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0</td>\n",
       "      <td>0.738206</td>\n",
       "      <td>0.405794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>E1</td>\n",
       "      <td>0.778198</td>\n",
       "      <td>0.561247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C1</td>\n",
       "      <td>0.787302</td>\n",
       "      <td>0.487215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L2</td>\n",
       "      <td>0.791204</td>\n",
       "      <td>0.423465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I1</td>\n",
       "      <td>0.810970</td>\n",
       "      <td>0.520213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>J1</td>\n",
       "      <td>0.840891</td>\n",
       "      <td>0.622481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1</td>\n",
       "      <td>0.882946</td>\n",
       "      <td>0.655279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H2</td>\n",
       "      <td>0.890143</td>\n",
       "      <td>0.662708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>K1</td>\n",
       "      <td>0.952905</td>\n",
       "      <td>0.908451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>K0</td>\n",
       "      <td>0.974743</td>\n",
       "      <td>0.957906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_type       auc        ks\n",
       "6          J0  0.637544  0.310224\n",
       "11         I0  0.657825  0.227697\n",
       "18         D2  0.666008  0.297143\n",
       "12         G2  0.668385  0.275986\n",
       "17         F2  0.675976  0.247202\n",
       "0          A2  0.700770  0.349880\n",
       "7          B2  0.710882  0.439071\n",
       "9          C0  0.721642  0.293451\n",
       "2          M0  0.737148  0.383970\n",
       "3          E0  0.738206  0.405794\n",
       "13         E1  0.778198  0.561247\n",
       "8          C1  0.787302  0.487215\n",
       "14         L2  0.791204  0.423465\n",
       "10         I1  0.810970  0.520213\n",
       "5          J1  0.840891  0.622481\n",
       "1          M1  0.882946  0.655279\n",
       "4          H2  0.890143  0.662708\n",
       "15         K1  0.952905  0.908451\n",
       "16         K0  0.974743  0.957906"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#批量计算AUC KS\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "\n",
    "#计算auc值 #计算roc值\n",
    "# model_type = 'A2' \n",
    "\n",
    "\n",
    "def ks_auc(model_type,d):\n",
    "    temp = d.loc[d.model_type == model_type]\n",
    "\n",
    "    fpr,tpr,thres = roc_curve(abs(temp.label -1),temp.esg_score) #好坏样本计算时要调换\n",
    "    roc_auc = auc(fpr,tpr)\n",
    "    # print('auc',roc_auc)\n",
    "\n",
    "    #计算ks值\n",
    "\n",
    "    ks = max(tpr-fpr)\n",
    "    # print('ks',ks)\n",
    "    dic = {}\n",
    "    dic['model_type'] = model_type\n",
    "    dic['auc'] = roc_auc\n",
    "    dic['ks'] = ks\n",
    "    return dic\n",
    "lst = []\n",
    "for model_type in d.model_type.unique():\n",
    "    lst.append(ks_auc(model_type,d))\n",
    "dfa = pd.DataFrame(lst)\n",
    "r5 = dfa#.to_csv(path + '各个模型的ks值与auc值.csv')\n",
    "r5 = r5.sort_values('auc')\n",
    "\n",
    "\n",
    "# plt.figure(figsize=(8,6))\n",
    "# plt.plot(r5.model_type,r5.auc)\n",
    "# plt.plot(r5.model_type,r5.ks)\n",
    "r5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c64f5f74-ebb9-463a-be04-160409f4a92c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_type</th>\n",
       "      <th>卡方值</th>\n",
       "      <th>p值</th>\n",
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       "      <th>分析对象</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>70.372950</td>\n",
       "      <td>8.584741e-09</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
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       "      <th>1</th>\n",
       "      <td>M1</td>\n",
       "      <td>177.256827</td>\n",
       "      <td>2.985634e-29</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
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       "      <th>2</th>\n",
       "      <td>M0</td>\n",
       "      <td>153.572068</td>\n",
       "      <td>1.540646e-24</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
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       "      <th>3</th>\n",
       "      <td>E0</td>\n",
       "      <td>123.082124</td>\n",
       "      <td>1.400364e-18</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H2</td>\n",
       "      <td>44.876036</td>\n",
       "      <td>1.082128e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>J1</td>\n",
       "      <td>104.801210</td>\n",
       "      <td>4.330135e-15</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>J0</td>\n",
       "      <td>119.456737</td>\n",
       "      <td>6.985882e-18</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B2</td>\n",
       "      <td>35.653516</td>\n",
       "      <td>3.231510e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C1</td>\n",
       "      <td>143.246113</td>\n",
       "      <td>1.322937e-24</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C0</td>\n",
       "      <td>72.919164</td>\n",
       "      <td>3.057803e-09</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I1</td>\n",
       "      <td>169.670887</td>\n",
       "      <td>9.793948e-28</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>I0</td>\n",
       "      <td>166.934143</td>\n",
       "      <td>3.438923e-27</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G2</td>\n",
       "      <td>193.009567</td>\n",
       "      <td>2.042906e-32</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>E1</td>\n",
       "      <td>83.710768</td>\n",
       "      <td>3.543294e-11</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L2</td>\n",
       "      <td>203.803294</td>\n",
       "      <td>1.349250e-34</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>K1</td>\n",
       "      <td>28.575720</td>\n",
       "      <td>9.532526e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>K0</td>\n",
       "      <td>137.795513</td>\n",
       "      <td>1.944706e-21</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F2</td>\n",
       "      <td>77.235419</td>\n",
       "      <td>5.219755e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>D2</td>\n",
       "      <td>32.381539</td>\n",
       "      <td>8.915745e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>五级分类</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   model_type         卡方值            p值  是否显著相关  分析对象\n",
       "0          A2   70.372950  8.584741e-09    True  五级分类\n",
       "1          M1  177.256827  2.985634e-29    True  五级分类\n",
       "2          M0  153.572068  1.540646e-24    True  五级分类\n",
       "3          E0  123.082124  1.400364e-18    True  五级分类\n",
       "4          H2   44.876036  1.082128e-05    True  五级分类\n",
       "5          J1  104.801210  4.330135e-15    True  五级分类\n",
       "6          J0  119.456737  6.985882e-18    True  五级分类\n",
       "7          B2   35.653516  3.231510e-03    True  五级分类\n",
       "8          C1  143.246113  1.322937e-24    True  五级分类\n",
       "9          C0   72.919164  3.057803e-09    True  五级分类\n",
       "10         I1  169.670887  9.793948e-28    True  五级分类\n",
       "11         I0  166.934143  3.438923e-27    True  五级分类\n",
       "12         G2  193.009567  2.042906e-32    True  五级分类\n",
       "13         E1   83.710768  3.543294e-11    True  五级分类\n",
       "14         L2  203.803294  1.349250e-34    True  五级分类\n",
       "15         K1   28.575720  9.532526e-06    True  五级分类\n",
       "16         K0  137.795513  1.944706e-21    True  五级分类\n",
       "17         F2   77.235419  5.219755e-10    True  五级分类\n",
       "18         D2   32.381539  8.915745e-03    True  五级分类"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>卡方值</th>\n",
       "      <th>p值</th>\n",
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       "      <th>分析对象</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>38.392318</td>\n",
       "      <td>9.299997e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1</td>\n",
       "      <td>64.473110</td>\n",
       "      <td>3.322446e-13</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M0</td>\n",
       "      <td>89.797275</td>\n",
       "      <td>1.454016e-18</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0</td>\n",
       "      <td>39.797582</td>\n",
       "      <td>4.766352e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H2</td>\n",
       "      <td>12.829417</td>\n",
       "      <td>1.214008e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>J1</td>\n",
       "      <td>97.424681</td>\n",
       "      <td>3.475071e-20</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>J0</td>\n",
       "      <td>31.205205</td>\n",
       "      <td>2.780046e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B2</td>\n",
       "      <td>3.812710</td>\n",
       "      <td>4.319457e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C1</td>\n",
       "      <td>79.407366</td>\n",
       "      <td>4.112913e-17</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C0</td>\n",
       "      <td>49.316367</td>\n",
       "      <td>5.015484e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I1</td>\n",
       "      <td>55.725198</td>\n",
       "      <td>2.289615e-11</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>I0</td>\n",
       "      <td>78.288507</td>\n",
       "      <td>4.013193e-16</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G2</td>\n",
       "      <td>91.858812</td>\n",
       "      <td>5.303437e-19</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>E1</td>\n",
       "      <td>23.357543</td>\n",
       "      <td>1.074116e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L2</td>\n",
       "      <td>113.824490</td>\n",
       "      <td>1.111979e-23</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>K1</td>\n",
       "      <td>13.274518</td>\n",
       "      <td>1.000950e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>K0</td>\n",
       "      <td>99.657914</td>\n",
       "      <td>1.163242e-20</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F2</td>\n",
       "      <td>44.346941</td>\n",
       "      <td>5.434695e-09</td>\n",
       "      <td>True</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>D2</td>\n",
       "      <td>7.097913</td>\n",
       "      <td>1.308036e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>是否逾期</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_type         卡方值            p值  是否显著相关  分析对象\n",
       "0          A2   38.392318  9.299997e-08    True  是否逾期\n",
       "1          M1   64.473110  3.322446e-13    True  是否逾期\n",
       "2          M0   89.797275  1.454016e-18    True  是否逾期\n",
       "3          E0   39.797582  4.766352e-08    True  是否逾期\n",
       "4          H2   12.829417  1.214008e-02    True  是否逾期\n",
       "5          J1   97.424681  3.475071e-20    True  是否逾期\n",
       "6          J0   31.205205  2.780046e-06    True  是否逾期\n",
       "7          B2    3.812710  4.319457e-01   False  是否逾期\n",
       "8          C1   79.407366  4.112913e-17    True  是否逾期\n",
       "9          C0   49.316367  5.015484e-10    True  是否逾期\n",
       "10         I1   55.725198  2.289615e-11    True  是否逾期\n",
       "11         I0   78.288507  4.013193e-16    True  是否逾期\n",
       "12         G2   91.858812  5.303437e-19    True  是否逾期\n",
       "13         E1   23.357543  1.074116e-04    True  是否逾期\n",
       "14         L2  113.824490  1.111979e-23    True  是否逾期\n",
       "15         K1   13.274518  1.000950e-02    True  是否逾期\n",
       "16         K0   99.657914  1.163242e-20    True  是否逾期\n",
       "17         F2   44.346941  5.434695e-09    True  是否逾期\n",
       "18         D2    7.097913  1.308036e-01   False  是否逾期"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#批量计算卡方值\n",
    "from scipy.stats import chi2_contingency\n",
    "# model_type = 'A2' \n",
    "\n",
    "def chi(model_type,d,target):\n",
    "    temp = d.loc[d.model_type == model_type]\n",
    "    di = {'ify':'是否逾期','level':'五级分类'}\n",
    "    \n",
    "    # temp\n",
    "    chi2,p,dof,expected = chi2_contingency(pd.crosstab(temp.esg_rating,temp[target]))\n",
    "    dic = {}\n",
    "    dic['model_type'] = model_type\n",
    "    dic['卡方值'] = chi2\n",
    "    dic['p值'] = p\n",
    "    dic['是否显著相关'] = p < 0.05\n",
    "    dic['分析对象'] = di[target]\n",
    "    return dic \n",
    "lst = []\n",
    "lst2 = []\n",
    "for model_type in d.model_type.unique():\n",
    "    # print(model_type)\n",
    "    lst.append(chi(model_type,d,'level'))\n",
    "    lst2.append(chi(model_type,d,'ify'))\n",
    "dfa = pd.DataFrame(lst)\n",
    "dfa2 = pd.DataFrame(lst2)\n",
    "\n",
    "\n",
    "display(dfa,dfa2)\n",
    "# dfa.to_csv(path + '各个模型esg等级与五级分类的相关性分析（卡方分析）.csv')\n",
    "# dfa2.to_csv(path + '各个模型esg等级与是否逾期的相关性分析（卡方分析）.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46b7aacd-d12a-491c-b8c7-5763c5909485",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt \n",
    "# plt.figure(figsize=(10,6))\n",
    "# plt.plot(fpr,tpr,lw=2)\n",
    "# plt.plot([0,1],[0,1],linestyle='--')\n",
    "# plt.xlim([0.0,1.0])\n",
    "# plt.ylim([0.0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14076459-6770-4f59-a237-f72c78adf3e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算头尾 A E二分    A，DE二分\n",
    "#好的里面有多少坏的 坏的里面有多少好的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d73a1db-e539-40fe-bd11-bc0120d88709",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_type</th>\n",
       "      <th>psi值</th>\n",
       "      <th>psi值是否小于0.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>0.110107</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1</td>\n",
       "      <td>0.185757</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M0</td>\n",
       "      <td>0.070568</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0</td>\n",
       "      <td>0.053393</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H2</td>\n",
       "      <td>0.039824</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>J1</td>\n",
       "      <td>0.154419</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>J0</td>\n",
       "      <td>0.094098</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.118700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C1</td>\n",
       "      <td>0.127293</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C0</td>\n",
       "      <td>0.041362</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I1</td>\n",
       "      <td>0.066844</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>I0</td>\n",
       "      <td>0.033963</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G2</td>\n",
       "      <td>0.056999</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>E1</td>\n",
       "      <td>0.119026</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L2</td>\n",
       "      <td>0.151348</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>K1</td>\n",
       "      <td>0.108223</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>K0</td>\n",
       "      <td>0.047357</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F2</td>\n",
       "      <td>0.076960</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>D2</td>\n",
       "      <td>0.096810</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_type      psi值  psi值是否小于0.2\n",
       "0          A2  0.110107         True\n",
       "1          M1  0.185757         True\n",
       "2          M0  0.070568         True\n",
       "3          E0  0.053393         True\n",
       "4          H2  0.039824         True\n",
       "5          J1  0.154419         True\n",
       "6          J0  0.094098         True\n",
       "7          B2  0.118700         True\n",
       "8          C1  0.127293         True\n",
       "9          C0  0.041362         True\n",
       "10         I1  0.066844         True\n",
       "11         I0  0.033963         True\n",
       "12         G2  0.056999         True\n",
       "13         E1  0.119026         True\n",
       "14         L2  0.151348         True\n",
       "15         K1  0.108223         True\n",
       "16         K0  0.047357         True\n",
       "17         F2  0.076960         True\n",
       "18         D2  0.096810         True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pl计算psi\n",
    "\n",
    "\n",
    "import toad  as td \n",
    "import pandas as pd\n",
    "df21 = pd.read_csv('./策略后esg分数2021.csv').rename(columns={'esg_score':'esg_score1'})\n",
    "df22 = pd.read_csv('./策略后esg分数2022.csv').rename(columns={'esg_score':'esg_score2'})\n",
    "del df21['model_type']\n",
    "d = pd.merge(df21,df22,on = 'custname')\n",
    "lst = []\n",
    "for model_type in d.model_type.unique():\n",
    "    def psi_(model_type,d):\n",
    "        temp = d.loc[d.model_type == model_type]\n",
    "        psi = td.metrics.PSI(temp.esg_score1,temp.esg_score2)\n",
    "        dic = {}\n",
    "        dic['model_type'] = model_type\n",
    "        dic['psi值'] =  psi \n",
    "        dic['psi值是否小于0.2'] = psi <= 0.2\n",
    "        return dic \n",
    "    lst.append(psi_(model_type,d))\n",
    "\n",
    "pd.DataFrame(lst)#.to_csv(path + '2021与2022两期分数的psi值计算(稳定性分析).csv')"
   ]
  }
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